Editors John Wainwright, Department of Geography, Durham University, UK Mark Mulligan, Department of Geography, King's College London, UK

• Preface to the Second Edition xiiiPreface to the First Edition xvList of Contributors xviiPart I Model Building 11 Introduction 3John Wainwright and Mark Mulligan1.1 Introduction 31.2 Why model the environment? 31.3 Why simplicity and complexity? 31.4 How to use this book 51.5 The book’s web site 6References 62 Modelling and Model Building 7Mark Mulligan and John Wainwright2.1 The role of modelling in environmental research 72.2 Approaches to model building: chickens, eggs, models and parameters? 122.3 Testing models 162.4 Sensitivity analysis and its role 182.5 Errors and uncertainty 202.6 Conclusions 23References 243 Time Series: Analysis and Modelling 27Bruce D. Malamud and Donald L. Turcotte3.1 Introduction 273.2 Examples of environmental time series 283.3 Frequency-size distribution of values in a time series 303.4 White noises and Brownian motions 323.5 Persistence 343.6 Other time-series models 413.7 Discussion and summary 41References 424 Non-Linear Dynamics Self-Organization and Cellular Automata Models 45David Favis-Mortlock4.1 Introduction 454.2 Self-organization in complex systems 474.3 Cellular automaton models 534.4 Case study: modelling rill initiation and growth 564.5 Summary and conclusions 614.6 Acknowledgements 63References 635 Spatial Modelling and Scaling Issues 69Xiaoyang Zhang Nick A. Drake and John Wainwright5.1 Introduction 695.2 Scale and scaling 705.3 Causes of scaling problems 715.4 Scaling issues of input parameters and possible solutions 725.5 Methodology for scaling physically based models 765.6 Scaling land-surface parameters for a soil-erosion model: a case study 825.7 Conclusion 84References 876 Environmental Applications of Computational Fluid Dynamics 91N.G. Wright and D.M. Hargreaves6.1 Introduction 916.2 CFD fundamentals 926.3 Applications of CFD in environmental modelling 976.4 Conclusions 104References 1067 Data-Based Mechanistic Modelling and the Emulation of Large Environmental System Models 111Peter C. Young and David Leedal7.1 Introduction 1117.2 Philosophies of science and modelling 1137.3 Statistical identification, estimation and validation 1137.4 Data-based mechanistic (DBM) modelling 1157.5 The statistical tools of DBM modelling 1177.6 Practical example 1177.7 The reduced-order modelling of large computer-simulation models 1227.8 The dynamic emulation of large computer-simulation models 1237.9 Conclusions 128References 1298 Stochastic versus Deterministic Approaches 133Philippe Renard, Andres Alcolea and David Ginsbourger8.1 Introduction 1338.2 A philosophical perspective 1358.3 Tools and methods 1378.4 A practical illustration in Oman 1438.5 Discussion 146References 148Part II The State of The Art in Environmental Modelling 1519 Climate and Climate-System Modelling 153L.D. Danny Harvey9.1 The complexity 1539.2 Finding the simplicity 1549.3 The research frontier 1599.4 Online material 160References 16310 Soil and Hillslope (Eco)Hydrology 165Andrew J. Baird10.1 Hillslope e-c-o-hydrology? 16510.2 Tyger tyger. . . 16910.3 Nobody loves me everybody hates me. . . 17210.4 Memories 17610.5 I’ll avoid you as long as I can? 17810.6 Acknowledgements 179References 18011 Modelling Catchment and Fluvial Processes and their Interactions 183Mark Mulligan and John Wainwright11.1 Introduction: connectivity in hydrology 18311.2 The complexity 18411.3 The simplicity 19611.4 Concluding remarks 201References 20112 Modelling Plant Ecology 207Rosie A. Fisher12.1 The complexity 20712.2 Finding the simplicity 20912.3 The research frontier 21212.4 Case study 21312.5 Conclusions 21712.6 Acknowledgements 217References 21813 Spatial Population Models for Animals 221George L.W. Perry and Nick R. Bond13.1 The complexity: introduction 22113.2 Finding the simplicity: thoughts on modelling spatial ecological systems 22213.3 The research frontier: marrying theory and practice 22713.4 Case study: dispersal dynamics in stream ecosystems 22813.5 Conclusions 23013.6 Acknowledgements 232References 23214 Vegetation and Disturbance 235Stefano Mazzoleni, Francisco Rego, Francesco Giannino Christian Ernest Vincenot, Gian Boris Pezzatti and Colin Legg14.1 The system complexity: effects of disturbance on vegetation dynamics 23514.2 The model simplification: simulation of plant growth under grazing and after fire 23714.3 New developments in ecological modelling 24014.4 Interactions of fire and grazing on plant competition: field experiment and modelling applications 24214.5 Conclusions 24714.6 Acknowledgements 248References 24815 Erosion and Sediment Transport: Finding Simplicity in a Complicated Erosion Model 253Richard E. Brazier15.1 The complexity 25315.2 Finding the simplicity 25315.3 WEPP – The Water Erosion Prediction Project 25415.4 MIRSED – a Minimum Information Requirement version of WEPP 25615.5 Data requirements 25815.6 Observed data describing erosion rates 25915.7 Mapping predicted erosion rates 25915.8 Comparison with published data 26215.9 Conclusions 264References 26416 Landslides Rockfalls and Sandpiles 267Stefan HergartenReferences 27517 Finding Simplicity in Complexity in Biogeochemical Modelling 277Hördur V. Haraldsson and Harald Sverdrup17.1 Introduction to models 27717.2 The basic classification of models 27817.3 A ‘good’ and a ‘bad’ model 27817.4 Dare to simplify 27917.5 Sorting 28017.6 The basic path 28217.7 The process 28317.8 Biogeochemical models 28317.9 Conclusion 288References 28818 Representing Human Decision-Making in Environmental Modelling 291James D.A. Millington, John Wainwright and Mark Mulligan18.1 Introduction 29118.2 Scenario approaches 29418.3 Economic modelling 29718.4 Agent-based modelling 30018.5 Discussion 304References 30519 Modelling Landscape Evolution 309Peter van der Beek19.1 Introduction 30919.2 Model setup and philosophy 31019.3 Geomorphic processes and model algorithms 31319.4 Model testing and calibration 31819.5 Coupling of models 32119.6 Model application: some examples 32119.7 Conclusions and outlook 324References 327Part III Models for Management 33320 Models Supporting Decision-Making and Policy Evaluation 335Mark Mulligan20.1 The complexity: making decisions and implementing policy in the real world 33520.2 The simplicity: state-of-the-art policy-support systems 34120.3 Addressing the remaining barriers 34520.4 Conclusions 34720.5 Acknowledgements 347References 34721 Models in Policy Formulation and Assessment: The WadBOS Decision-Support System 349Guy Engelen21.1 Introduction 34921.2 Functions of WadBOS 35021.3 Decision-support systems 35121.4 Building the integrated model 35121.5 The integrated WadBOS model 35421.6 The toolbase 35921.7 The database 35921.8 The user-interface 36021.9 Discussion and conclusions 36221.10 Acknowledgments 363References 36322 Soil Erosion and Conservation 365Mark A. Nearing22.1 The problem 36522.2 The approaches 36722.3 The contributions of modelling 36922.4 Lessons and implications 37522.5 Acknowledgements 376References 37623 Forest-Management Modelling 379Mark J. Twery and Aaron R. Weiskittel23.1 The issue 37923.2 The approaches 37923.3 Components of empirical models 38323.4 Implementation and use 38623.5 Example model 39023.6 Lessons and implications 390References 39124 Stability and Instability in the Management of Mediterranean Desertification 399John B. Thornes24.1 Introduction 39924.2 Basic propositions 40024.3 Complex interactions 40324.4 Climate gradient and climate change 40824.5 Implications 40924.6 Plants 41024.7 Lessons and implications 411References 41125 Operational European Flood Forecasting 415Hannah Cloke, Florian Pappenberger, Jutta Thielen and Vera Thiemig25.1 The problem: providing early flood warning at the European scale 41525.2 Flood forecasting at the European scale: the approaches 41625.3 The European Flood Alert System (EFAS) 42225.4 Lessons and implications 429References 43026 Assessing Model Adequacy 435Michael Goldstein Allan Seheult and Ian Vernon26.1 Introduction 43526.2 General issues in assessing model adequacy 43526.3 Assessing model adequacy for a fast rainfall-runoff model 43826.4 Slow computer models 44626.5 Acknowledgements 449References 449Part IV Current and Future Developments 45127 Pointers for the Future 453John Wainwright and Mark Mulligan27.1 What have we learned? 45327.2 Research directions 45927.3 Technological directions 45927.4 Is it possible to find simplicity in complexity? 463References 463Index 465

“Those caveats aside, this book will provide an interesting and stimulating read for scientists with some familiarity with modelling who want to extend their understanding and to see how modelling has been usefully applied across a very wide range of problems in environmental science.”  (European Journal of Soil Science, 1 December 2013)“Summing Up: Recommended.  Graduate students, researchers/faculty, and professionals/practitioners.”  (Choice, 1 January 2014)“To conclude, the book offers important information on how to use models to develop our understanding of the processes that form the environment around us.”  (Environmental Engineering and Management Journal, 1 April 2013)

Editors John Wainwright, Department of Geography, Durham University, UK Mark Mulligan, Department of Geography, King's College London, UK

• Preface to the Second Edition xiiiPreface to the First Edition xvList of Contributors xviiPart I Model Building 11 Introduction 3John Wainwright and Mark Mulligan1.1 Introduction 31.2 Why model the environment? 31.3 Why simplicity and complexity? 31.4 How to use this book 51.5 The book’s web site 6References 62 Modelling and Model Building 7Mark Mulligan and John Wainwright2.1 The role of modelling in environmental research 72.2 Approaches to model building: chickens, eggs, models and parameters? 122.3 Testing models 162.4 Sensitivity analysis and its role 182.5 Errors and uncertainty 202.6 Conclusions 23References 243 Time Series: Analysis and Modelling 27Bruce D. Malamud and Donald L. Turcotte3.1 Introduction 273.2 Examples of environmental time series 283.3 Frequency-size distribution of values in a time series 303.4 White noises and Brownian motions 323.5 Persistence 343.6 Other time-series models 413.7 Discussion and summary 41References 424 Non-Linear Dynamics Self-Organization and Cellular Automata Models 45David Favis-Mortlock4.1 Introduction 454.2 Self-organization in complex systems 474.3 Cellular automaton models 534.4 Case study: modelling rill initiation and growth 564.5 Summary and conclusions 614.6 Acknowledgements 63References 635 Spatial Modelling and Scaling Issues 69Xiaoyang Zhang Nick A. Drake and John Wainwright5.1 Introduction 695.2 Scale and scaling 705.3 Causes of scaling problems 715.4 Scaling issues of input parameters and possible solutions 725.5 Methodology for scaling physically based models 765.6 Scaling land-surface parameters for a soil-erosion model: a case study 825.7 Conclusion 84References 876 Environmental Applications of Computational Fluid Dynamics 91N.G. Wright and D.M. Hargreaves6.1 Introduction 916.2 CFD fundamentals 926.3 Applications of CFD in environmental modelling 976.4 Conclusions 104References 1067 Data-Based Mechanistic Modelling and the Emulation of Large Environmental System Models 111Peter C. Young and David Leedal7.1 Introduction 1117.2 Philosophies of science and modelling 1137.3 Statistical identification, estimation and validation 1137.4 Data-based mechanistic (DBM) modelling 1157.5 The statistical tools of DBM modelling 1177.6 Practical example 1177.7 The reduced-order modelling of large computer-simulation models 1227.8 The dynamic emulation of large computer-simulation models 1237.9 Conclusions 128References 1298 Stochastic versus Deterministic Approaches 133Philippe Renard, Andres Alcolea and David Ginsbourger8.1 Introduction 1338.2 A philosophical perspective 1358.3 Tools and methods 1378.4 A practical illustration in Oman 1438.5 Discussion 146References 148Part II The State of The Art in Environmental Modelling 1519 Climate and Climate-System Modelling 153L.D. Danny Harvey9.1 The complexity 1539.2 Finding the simplicity 1549.3 The research frontier 1599.4 Online material 160References 16310 Soil and Hillslope (Eco)Hydrology 165Andrew J. Baird10.1 Hillslope e-c-o-hydrology? 16510.2 Tyger tyger. . . 16910.3 Nobody loves me everybody hates me. . . 17210.4 Memories 17610.5 I’ll avoid you as long as I can? 17810.6 Acknowledgements 179References 18011 Modelling Catchment and Fluvial Processes and their Interactions 183Mark Mulligan and John Wainwright11.1 Introduction: connectivity in hydrology 18311.2 The complexity 18411.3 The simplicity 19611.4 Concluding remarks 201References 20112 Modelling Plant Ecology 207Rosie A. Fisher12.1 The complexity 20712.2 Finding the simplicity 20912.3 The research frontier 21212.4 Case study 21312.5 Conclusions 21712.6 Acknowledgements 217References 21813 Spatial Population Models for Animals 221George L.W. Perry and Nick R. Bond13.1 The complexity: introduction 22113.2 Finding the simplicity: thoughts on modelling spatial ecological systems 22213.3 The research frontier: marrying theory and practice 22713.4 Case study: dispersal dynamics in stream ecosystems 22813.5 Conclusions 23013.6 Acknowledgements 232References 23214 Vegetation and Disturbance 235Stefano Mazzoleni, Francisco Rego, Francesco Giannino Christian Ernest Vincenot, Gian Boris Pezzatti and Colin Legg14.1 The system complexity: effects of disturbance on vegetation dynamics 23514.2 The model simplification: simulation of plant growth under grazing and after fire 23714.3 New developments in ecological modelling 24014.4 Interactions of fire and grazing on plant competition: field experiment and modelling applications 24214.5 Conclusions 24714.6 Acknowledgements 248References 24815 Erosion and Sediment Transport: Finding Simplicity in a Complicated Erosion Model 253Richard E. Brazier15.1 The complexity 25315.2 Finding the simplicity 25315.3 WEPP – The Water Erosion Prediction Project 25415.4 MIRSED – a Minimum Information Requirement version of WEPP 25615.5 Data requirements 25815.6 Observed data describing erosion rates 25915.7 Mapping predicted erosion rates 25915.8 Comparison with published data 26215.9 Conclusions 264References 26416 Landslides Rockfalls and Sandpiles 267Stefan HergartenReferences 27517 Finding Simplicity in Complexity in Biogeochemical Modelling 277Hördur V. Haraldsson and Harald Sverdrup17.1 Introduction to models 27717.2 The basic classification of models 27817.3 A ‘good’ and a ‘bad’ model 27817.4 Dare to simplify 27917.5 Sorting 28017.6 The basic path 28217.7 The process 28317.8 Biogeochemical models 28317.9 Conclusion 288References 28818 Representing Human Decision-Making in Environmental Modelling 291James D.A. Millington, John Wainwright and Mark Mulligan18.1 Introduction 29118.2 Scenario approaches 29418.3 Economic modelling 29718.4 Agent-based modelling 30018.5 Discussion 304References 30519 Modelling Landscape Evolution 309Peter van der Beek19.1 Introduction 30919.2 Model setup and philosophy 31019.3 Geomorphic processes and model algorithms 31319.4 Model testing and calibration 31819.5 Coupling of models 32119.6 Model application: some examples 32119.7 Conclusions and outlook 324References 327Part III Models for Management 33320 Models Supporting Decision-Making and Policy Evaluation 335Mark Mulligan20.1 The complexity: making decisions and implementing policy in the real world 33520.2 The simplicity: state-of-the-art policy-support systems 34120.3 Addressing the remaining barriers 34520.4 Conclusions 34720.5 Acknowledgements 347References 34721 Models in Policy Formulation and Assessment: The WadBOS Decision-Support System 349Guy Engelen21.1 Introduction 34921.2 Functions of WadBOS 35021.3 Decision-support systems 35121.4 Building the integrated model 35121.5 The integrated WadBOS model 35421.6 The toolbase 35921.7 The database 35921.8 The user-interface 36021.9 Discussion and conclusions 36221.10 Acknowledgments 363References 36322 Soil Erosion and Conservation 365Mark A. Nearing22.1 The problem 36522.2 The approaches 36722.3 The contributions of modelling 36922.4 Lessons and implications 37522.5 Acknowledgements 376References 37623 Forest-Management Modelling 379Mark J. Twery and Aaron R. Weiskittel23.1 The issue 37923.2 The approaches 37923.3 Components of empirical models 38323.4 Implementation and use 38623.5 Example model 39023.6 Lessons and implications 390References 39124 Stability and Instability in the Management of Mediterranean Desertification 399John B. Thornes24.1 Introduction 39924.2 Basic propositions 40024.3 Complex interactions 40324.4 Climate gradient and climate change 40824.5 Implications 40924.6 Plants 41024.7 Lessons and implications 411References 41125 Operational European Flood Forecasting 415Hannah Cloke, Florian Pappenberger, Jutta Thielen and Vera Thiemig25.1 The problem: providing early flood warning at the European scale 41525.2 Flood forecasting at the European scale: the approaches 41625.3 The European Flood Alert System (EFAS) 42225.4 Lessons and implications 429References 43026 Assessing Model Adequacy 435Michael Goldstein Allan Seheult and Ian Vernon26.1 Introduction 43526.2 General issues in assessing model adequacy 43526.3 Assessing model adequacy for a fast rainfall-runoff model 43826.4 Slow computer models 44626.5 Acknowledgements 449References 449Part IV Current and Future Developments 45127 Pointers for the Future 453John Wainwright and Mark Mulligan27.1 What have we learned? 45327.2 Research directions 45927.3 Technological directions 45927.4 Is it possible to find simplicity in complexity? 463References 463Index 465

“Those caveats aside, this book will provide an interesting and stimulating read for scientists with some familiarity with modelling who want to extend their understanding and to see how modelling has been usefully applied across a very wide range of problems in environmental science.”  (European Journal of Soil Science, 1 December 2013)“Summing Up: Recommended.  Graduate students, researchers/faculty, and professionals/practitioners.”  (Choice, 1 January 2014)“To conclude, the book offers important information on how to use models to develop our understanding of the processes that form the environment around us.”  (Environmental Engineering and Management Journal, 1 April 2013)

Editors John Wainwright, Department of Geography, Durham University, UK Mark Mulligan, Department of Geography, King's College London, UK

• Preface to the Second Edition xiiiPreface to the First Edition xvList of Contributors xviiPart I Model Building 11 Introduction 3John Wainwright and Mark Mulligan1.1 Introduction 31.2 Why model the environment? 31.3 Why simplicity and complexity? 31.4 How to use this book 51.5 The book’s web site 6References 62 Modelling and Model Building 7Mark Mulligan and John Wainwright2.1 The role of modelling in environmental research 72.2 Approaches to model building: chickens, eggs, models and parameters? 122.3 Testing models 162.4 Sensitivity analysis and its role 182.5 Errors and uncertainty 202.6 Conclusions 23References 243 Time Series: Analysis and Modelling 27Bruce D. Malamud and Donald L. Turcotte3.1 Introduction 273.2 Examples of environmental time series 283.3 Frequency-size distribution of values in a time series 303.4 White noises and Brownian motions 323.5 Persistence 343.6 Other time-series models 413.7 Discussion and summary 41References 424 Non-Linear Dynamics Self-Organization and Cellular Automata Models 45David Favis-Mortlock4.1 Introduction 454.2 Self-organization in complex systems 474.3 Cellular automaton models 534.4 Case study: modelling rill initiation and growth 564.5 Summary and conclusions 614.6 Acknowledgements 63References 635 Spatial Modelling and Scaling Issues 69Xiaoyang Zhang Nick A. Drake and John Wainwright5.1 Introduction 695.2 Scale and scaling 705.3 Causes of scaling problems 715.4 Scaling issues of input parameters and possible solutions 725.5 Methodology for scaling physically based models 765.6 Scaling land-surface parameters for a soil-erosion model: a case study 825.7 Conclusion 84References 876 Environmental Applications of Computational Fluid Dynamics 91N.G. Wright and D.M. Hargreaves6.1 Introduction 916.2 CFD fundamentals 926.3 Applications of CFD in environmental modelling 976.4 Conclusions 104References 1067 Data-Based Mechanistic Modelling and the Emulation of Large Environmental System Models 111Peter C. Young and David Leedal7.1 Introduction 1117.2 Philosophies of science and modelling 1137.3 Statistical identification, estimation and validation 1137.4 Data-based mechanistic (DBM) modelling 1157.5 The statistical tools of DBM modelling 1177.6 Practical example 1177.7 The reduced-order modelling of large computer-simulation models 1227.8 The dynamic emulation of large computer-simulation models 1237.9 Conclusions 128References 1298 Stochastic versus Deterministic Approaches 133Philippe Renard, Andres Alcolea and David Ginsbourger8.1 Introduction 1338.2 A philosophical perspective 1358.3 Tools and methods 1378.4 A practical illustration in Oman 1438.5 Discussion 146References 148Part II The State of The Art in Environmental Modelling 1519 Climate and Climate-System Modelling 153L.D. Danny Harvey9.1 The complexity 1539.2 Finding the simplicity 1549.3 The research frontier 1599.4 Online material 160References 16310 Soil and Hillslope (Eco)Hydrology 165Andrew J. Baird10.1 Hillslope e-c-o-hydrology? 16510.2 Tyger tyger. . . 16910.3 Nobody loves me everybody hates me. . . 17210.4 Memories 17610.5 I’ll avoid you as long as I can? 17810.6 Acknowledgements 179References 18011 Modelling Catchment and Fluvial Processes and their Interactions 183Mark Mulligan and John Wainwright11.1 Introduction: connectivity in hydrology 18311.2 The complexity 18411.3 The simplicity 19611.4 Concluding remarks 201References 20112 Modelling Plant Ecology 207Rosie A. Fisher12.1 The complexity 20712.2 Finding the simplicity 20912.3 The research frontier 21212.4 Case study 21312.5 Conclusions 21712.6 Acknowledgements 217References 21813 Spatial Population Models for Animals 221George L.W. Perry and Nick R. Bond13.1 The complexity: introduction 22113.2 Finding the simplicity: thoughts on modelling spatial ecological systems 22213.3 The research frontier: marrying theory and practice 22713.4 Case study: dispersal dynamics in stream ecosystems 22813.5 Conclusions 23013.6 Acknowledgements 232References 23214 Vegetation and Disturbance 235Stefano Mazzoleni, Francisco Rego, Francesco Giannino Christian Ernest Vincenot, Gian Boris Pezzatti and Colin Legg14.1 The system complexity: effects of disturbance on vegetation dynamics 23514.2 The model simplification: simulation of plant growth under grazing and after fire 23714.3 New developments in ecological modelling 24014.4 Interactions of fire and grazing on plant competition: field experiment and modelling applications 24214.5 Conclusions 24714.6 Acknowledgements 248References 24815 Erosion and Sediment Transport: Finding Simplicity in a Complicated Erosion Model 253Richard E. Brazier15.1 The complexity 25315.2 Finding the simplicity 25315.3 WEPP – The Water Erosion Prediction Project 25415.4 MIRSED – a Minimum Information Requirement version of WEPP 25615.5 Data requirements 25815.6 Observed data describing erosion rates 25915.7 Mapping predicted erosion rates 25915.8 Comparison with published data 26215.9 Conclusions 264References 26416 Landslides Rockfalls and Sandpiles 267Stefan HergartenReferences 27517 Finding Simplicity in Complexity in Biogeochemical Modelling 277Hördur V. Haraldsson and Harald Sverdrup17.1 Introduction to models 27717.2 The basic classification of models 27817.3 A ‘good’ and a ‘bad’ model 27817.4 Dare to simplify 27917.5 Sorting 28017.6 The basic path 28217.7 The process 28317.8 Biogeochemical models 28317.9 Conclusion 288References 28818 Representing Human Decision-Making in Environmental Modelling 291James D.A. Millington, John Wainwright and Mark Mulligan18.1 Introduction 29118.2 Scenario approaches 29418.3 Economic modelling 29718.4 Agent-based modelling 30018.5 Discussion 304References 30519 Modelling Landscape Evolution 309Peter van der Beek19.1 Introduction 30919.2 Model setup and philosophy 31019.3 Geomorphic processes and model algorithms 31319.4 Model testing and calibration 31819.5 Coupling of models 32119.6 Model application: some examples 32119.7 Conclusions and outlook 324References 327Part III Models for Management 33320 Models Supporting Decision-Making and Policy Evaluation 335Mark Mulligan20.1 The complexity: making decisions and implementing policy in the real world 33520.2 The simplicity: state-of-the-art policy-support systems 34120.3 Addressing the remaining barriers 34520.4 Conclusions 34720.5 Acknowledgements 347References 34721 Models in Policy Formulation and Assessment: The WadBOS Decision-Support System 349Guy Engelen21.1 Introduction 34921.2 Functions of WadBOS 35021.3 Decision-support systems 35121.4 Building the integrated model 35121.5 The integrated WadBOS model 35421.6 The toolbase 35921.7 The database 35921.8 The user-interface 36021.9 Discussion and conclusions 36221.10 Acknowledgments 363References 36322 Soil Erosion and Conservation 365Mark A. Nearing22.1 The problem 36522.2 The approaches 36722.3 The contributions of modelling 36922.4 Lessons and implications 37522.5 Acknowledgements 376References 37623 Forest-Management Modelling 379Mark J. Twery and Aaron R. Weiskittel23.1 The issue 37923.2 The approaches 37923.3 Components of empirical models 38323.4 Implementation and use 38623.5 Example model 39023.6 Lessons and implications 390References 39124 Stability and Instability in the Management of Mediterranean Desertification 399John B. Thornes24.1 Introduction 39924.2 Basic propositions 40024.3 Complex interactions 40324.4 Climate gradient and climate change 40824.5 Implications 40924.6 Plants 41024.7 Lessons and implications 411References 41125 Operational European Flood Forecasting 415Hannah Cloke, Florian Pappenberger, Jutta Thielen and Vera Thiemig25.1 The problem: providing early flood warning at the European scale 41525.2 Flood forecasting at the European scale: the approaches 41625.3 The European Flood Alert System (EFAS) 42225.4 Lessons and implications 429References 43026 Assessing Model Adequacy 435Michael Goldstein Allan Seheult and Ian Vernon26.1 Introduction 43526.2 General issues in assessing model adequacy 43526.3 Assessing model adequacy for a fast rainfall-runoff model 43826.4 Slow computer models 44626.5 Acknowledgements 449References 449Part IV Current and Future Developments 45127 Pointers for the Future 453John Wainwright and Mark Mulligan27.1 What have we learned? 45327.2 Research directions 45927.3 Technological directions 45927.4 Is it possible to find simplicity in complexity? 463References 463Index 465

“Those caveats aside, this book will provide an interesting and stimulating read for scientists with some familiarity with modelling who want to extend their understanding and to see how modelling has been usefully applied across a very wide range of problems in environmental science.”  (European Journal of Soil Science, 1 December 2013)“Summing Up: Recommended.  Graduate students, researchers/faculty, and professionals/practitioners.”  (Choice, 1 January 2014)“To conclude, the book offers important information on how to use models to develop our understanding of the processes that form the environment around us.”  (Environmental Engineering and Management Journal, 1 April 2013)